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Week 7 Discussion Forum Discussion Prompt This week we’re exploring causation and correlation. • Why is it a fallacy to confuse causation and correlation? • Provide an example of a statement that confuses causation with correlation. In addition to your inital post, you must also post substantive responses to at least two of your classmates’ posts in this thread. Provide an analysis of your peers’ post. Build on their examples and explanations to extend meaningful discussion. =========================================================== Kickoff Post Welcome to the Week 7 Discussion Forum! A few weeks ago when we were discussing logical fallacies, we encountered the fallacies post hoc ergo propter hoc, that is, “after this, therefore because of this” – i.e. the mistaken belief that simply because phenomenon B follows phenomenon A, that means that A must have caused B, when they might both be due to some other common cause, or the association may be entirely random, such as in the image below: If you’re trying to determine the causation here – for example, “cheese consumption causes uneasy dreams, which causes people to move around a lot in bed, which can lead to people becoming tangled in their bedsheets” – I don’t blame you. The human brain is designed to identify patterns – that’s how we survived and evolved. But that function is often overactive and finds patterns that aren’t really there, such as in this case. There is no causal relationship between these two phenomena – it’s entirely random, and that’s not that big a deal since think of the infinite number of phenomena in the world – there are bound to be random patterns that happen to correspond to each other without any actual connection. Here are some other fun examples. We also encountered earlier the fallacy cum hoc ergo propter hoc (“with this, therefore because of this” – i.e. the mistaken belief that because two things are frequently correlated, one must be the cause of the other, when again the association may be random, or it may instead be that they’re both caused by something else, such as in the example below). That was an early preview to a central insight in the natural and social sciences: correlation does not imply causation. Just because some behavior happens to frequently or always accompany another behavior does not in itself prove that one is the cause of the other. Causation is notoriously hard to prove, and responsible scientists recognize that, once we recognize a correlation between two discrete phenomena, a lot more work has to be done to determine if there’s any sort of causal link. So let’s hear some more good examples of mistakes that people make in confusing causation with correlation. In your responses to classmates, one approach can be to identify what steps would need to be taken in order to determine causation – i.e. not necessarily between the two phenomena named, but to determine what other thing might be causing the two things together, or if we can determine whether there’s any relationship at all (and keep in mind that your responses need to add to the conversation – it’s nice when you say nice things to each other, but don’t stop there, since that alone doesn’t add much substance to the conversation, and the goal here is to keep the conversation moving forward thoughtfully!).